Electromagnetic waves are classified into several types depending on the wave frequency. These waves have been applied to a lot of applications like in-vehicle radar devices for measuring the distance between moving vehicles in order to prevent collisions, in concealed weapon detection, or in detecting malignant cells. Further, improved generation and detection techniques as well as latest improvements in the integration and miniaturization of devices operating at various frequency ranges have created a lot of interest to exploit the properties of this electromagnetic radiation. Examples are millimeter and sub-millimeter waves (30 GHz to a few THz) which have the ability to penetrate non-metal materials, including plastics, walls, clothes, smoke and fog.
Electromagnetic waves can be used in an active or a passive mode. A passive radiometric imaging system creates images by capturing the electromagnetic radiation emitted by the objects by using a radiometer. Such a passive radiometric imaging system is, for instance, known from US 2007/0221847 A1.
Passive electromagnetic waves are emitted naturally by any object. The strengths of these waves depend on the object itself. These signals are, however, weak and are degraded fast due to both the internal noise factors of the radiometer and the external noise factors of the environment. Therefore, the radiation samples of the passive electromagnetic waves captured by the radiometer are generally degraded by high noise variations.
Conventionally, in passive radiometric imaging devices it is tried to employ image de-noising and enhancement algorithms to reduce the effect of the noisy images obtained from the sensor (i.e. the radiometer). Such de-noising or enhancement algorithms can be based on image wavelets, on the Total Variation principle, on manifold learning algorithms, on filtering schemes like wiener or bilateral filtering, on diffusion algorithms or on image pyramids in combination with extrapolation in the frequency space. Many other techniques exist as a state of the art. However, all of these algorithms are mainly designed as a post-processing step of the noisy radiometer image. In other words, such algorithms do not directly process the radiometric samples when recovering the radiometer image, but they enhance, improve or de-noise an integrated image from all the samples of the radiometer by using an algorithm like one of the just mentioned algorithms.
Further, these techniques can suppress the noise to some extent but the resulting image will suffer from a lot of fluctuations due to the high variation of noise and interference at the sensor, especially in passive radiometers, in which the signal to noise ratio is low.
As a result, the obtained image after reconstruction will be degraded. This will make the applications of the passive radiometric imaging device for object detection in security screening applications a difficult task. This is mainly due to the degraded nature of the image that can result either in false alarms or in some suspicious objects being missed from the screening.